Implement NPC memory and interaction system with SQLite database; add NPC data structure and dynamic NPC handling; integrate LLM for NPC conversations and quest generation.

utils/npc_handler.py

@@ -0,0 +1,100 @@
+import json
+import random
+import requests
+from .npc_data import NPC_DATABASE  # <-- Add this import
+
+def query_llm(prompt, model="neural-chat", url="http://100.103.117.14:11434/api/generate"):
+    # Add instruction for short answers
+    short_prompt = prompt + "\n\nKeep your answer short and concise (1-2 sentences)."
+    payload = {
+        "model": model,
+        "prompt": short_prompt,
+        "stream": False
+    }
+    try:
+        response = requests.post(url, json=payload, timeout=120)
+        response.raise_for_status()
+        data = response.json()
+        return data.get("response", "").strip()
+    except Exception as e:
+        print(f"LLM error: {e}")
+        return None
+
+class DynamicNPC:
+    def __init__(self, name, data):
+        self.name = name
+        self.personality = data["personality"]
+        self.backstory = data["backstory"]
+        self.quirks = data["quirks"]
+
+class NPCHandler:
+    def __init__(self, memory):
+        self.memory = memory
+        self.npcs = {name: DynamicNPC(name, data) for name, data in NPC_DATABASE.items()}
+
+    def chat_with_npc(self, npc_name, message, player_context):
+        npc = self.npcs.get(npc_name)
+        if not npc:
+            return "That NPC doesn't exist."
+
+        player_id = player_context['id']
+
+        # Fetch recent conversation history (last 3 exchanges)
+        history = self.memory.get_conversation(npc_name, player_id)
+        history_str = ""
+        if history:
+            history_str = "\nRecent conversation:\n"
+            for player_msg, npc_reply in history[-3:]:
+                history_str += f"Player: {player_msg}\n{npc.name}: {npc_reply}\n"
+
+        # Fetch affinity
+        affinity = self.memory.get_affinity(npc_name, player_id)
+        affinity_str = f"Affinity with player: {affinity}\n"
+
+        prompt = (
+            f"You are {npc.name}, an NPC in a fantasy world.\n"
+            f"Personality: {npc.personality}\n"
+            f"Backstory: {npc.backstory}\n"
+            f"Quirks: {', '.join(npc.quirks)}\n"
+            f"{history_str}"
+            f"{affinity_str}"
+            f"Player (level {player_context.get('level', 1)}): {message}\n"
+            f"Respond in character as {npc.name}."
+        )
+        llm_response = query_llm(prompt)
+        if not llm_response:
+            llm_response = f"{npc.name} seems lost in thought and doesn't reply."
+        self.memory.log_conversation(npc_name, player_id, message, llm_response)
+        self.memory.update_affinity(npc_name, player_id, 1)
+        return llm_response
+
+    def generate_quest(self, npc_name, player_level):
+        npc = self.npcs.get(npc_name)
+        if not npc:
+            return None
+        prompt = (
+            f"As {npc.name}, create a short quest for a level {player_level} adventurer.\n"
+            f"Personality: {npc.personality}\n"
+            f"Backstory: {npc.backstory}\n"
+            f"Format as JSON:\n"
+            "{{\n"
+            '  "title": "Quest name",\n'
+            '  "description": "What the player must do",\n'
+            '  "reward": "coins/items/reputation",\n'
+            '  "difficulty": "easy/medium/hard"\n'
+            "}}"
+        )
+        llm_response = query_llm(prompt)
+        try:
+            if not llm_response:
+                raise ValueError("No response from LLM")
+            quest = json.loads(llm_response)
+            return quest
+        except Exception:
+            quest = {
+                "title": f"{npc.name}'s Request",
+                "description": f"Help {npc.name} with a task suitable for level {player_level}.",
+                "reward": f"{random.randint(10, 100)} coins",
+                "difficulty": random.choice(["easy", "medium", "hard"])
+            }
+            return quest